The operator who builds your GTM operating system

I started Define Data because I kept seeing the same thing in scaling companies: they had bought the tools, but the GTM system underneath did not hold together. CRM architecture drifted, lifecycle and handoffs leaked, attribution was guesswork, and reporting quietly stopped being trusted. They did not need another platform — they needed someone who had built this before and could turn it into a source of truth. That is what I do, and for the bigger builds I bring in trusted specialists I have worked with over the years.

I have spent over a decade as a Head of Data, Analytics and Business Systems — building the function, fixing broken reporting, and getting leadership to actually trust the numbers in front of them. I have done it in high-growth SaaS, in regulated banking, and in e-commerce operating across 14 markets.

At a Series C fintech I built the Data & Analytics function from scratch — hiring the team, standing up the platform, and delivering the reporting that sales, finance and product leadership relied on daily. When the CRM and business systems needed work, I took that on too: redesigning Salesforce reporting, deploying and integrating HubSpot marketing, improving pipeline data quality by 80%, and replacing monthly Excel packs with daily self-serve analytics used by every manager in the business.

Before that I led digital analytics for a €50 million banking initiative, working across 1.5 billion events a year from more than a million customers and presenting insight straight to the steering committee. In e-commerce I grew from analyst to leading a team of analysts and data scientists across a €500 million revenue business operating in 14 markets.

Most recently I led Data & AI, Product and Engineering at a funded AI startup — building a multi-tenant data platform handling 20+ enterprise customer workloads and shipping agentic AI workflows with a 15-person team. That is the same span — GTM, data, systems and AI — that scaling companies have to get working together, and where the drag tends to hide.